
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






def 				_lowercase				(	UpperCamelCase_						)						->						Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					np.inf

					def set_batch_size(UpperCamelCase_						) -> None:
										nonlocal batch_size
										if isinstance(a__      ,      a__						):
															SCREAMING_SNAKE_CASE__						=					min(a__      ,      config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS						)
										elif isinstance(a__      ,      a__						):
															SCREAMING_SNAKE_CASE__						=					min(a__      ,      config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS						)
										elif isinstance(a__      ,      a__						) and feature.dtype == "binary":
															SCREAMING_SNAKE_CASE__						=					min(a__      ,      config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS						)

					_visit(a__      ,      a__						)

					return None if batch_size is np.inf else batch_size




class 		lowercase__    (		_UpperCAmelCase    ):
	def __init__(     self				:  List[str]			,  UpperCAmelCase_				:  NestedDataStructureLike[PathLike]			,  UpperCAmelCase_				:  Optional[NamedSplit] = None			,  UpperCAmelCase_				:  Optional[Features] = None			,  UpperCAmelCase_				:  str = None			,  UpperCAmelCase_				:  bool = False			,  UpperCAmelCase_				:  bool = False			,  UpperCAmelCase_				:  Optional[int] = None			,  **UpperCAmelCase_				:  Dict			,  ):
						super().__init__(
						    UpperCAmelCase_			,  split=UpperCAmelCase_			,  features=UpperCAmelCase_			,  cache_dir=UpperCAmelCase_			,  keep_in_memory=UpperCAmelCase_			,  streaming=UpperCAmelCase_			,  num_proc=UpperCAmelCase_			,  **UpperCAmelCase_			,  )
						SCREAMING_SNAKE_CASE__						=					path_or_paths if isinstance(UpperCAmelCase_			,  UpperCAmelCase_				) else {self.split: path_or_paths}
						SCREAMING_SNAKE_CASE__						=					_PACKAGED_DATASETS_MODULES['parquet'][1]
						SCREAMING_SNAKE_CASE__						=					Parquet(
						    cache_dir=UpperCAmelCase_			,  data_files=UpperCAmelCase_			,  features=UpperCAmelCase_			,  hash=UpperCAmelCase_			,  **UpperCAmelCase_			,  )





	def 	A_							(     self				:  Any				):
						# Build iterable dataset
						if self.streaming:
											SCREAMING_SNAKE_CASE__						=					self.builder.as_streaming_dataset(split=self.split				)
						# Build regular (map-style) dataset
						else:
											SCREAMING_SNAKE_CASE__						=					None
											SCREAMING_SNAKE_CASE__						=					None
											SCREAMING_SNAKE_CASE__						=					None
											SCREAMING_SNAKE_CASE__						=					None

											self.builder.download_and_prepare(
											    download_config=UpperCAmelCase_			,  download_mode=UpperCAmelCase_			,  verification_mode=UpperCAmelCase_			,  base_path=UpperCAmelCase_			,  num_proc=self.num_proc			,  )
											SCREAMING_SNAKE_CASE__						=					self.builder.as_dataset(
											    split=self.split			,  verification_mode=UpperCAmelCase_			,  in_memory=self.keep_in_memory				)
						return dataset





class 		lowercase__    :
	def __init__(     self				:  Optional[int]			,  UpperCAmelCase_				:  Dataset			,  UpperCAmelCase_				:  Union[PathLike, BinaryIO]			,  UpperCAmelCase_				:  Optional[int] = None			,  **UpperCAmelCase_				:  Optional[Any]			,  ):
						SCREAMING_SNAKE_CASE__						=					dataset
						SCREAMING_SNAKE_CASE__						=					path_or_buf
						SCREAMING_SNAKE_CASE__						=					batch_size or get_writer_batch_size(dataset.features				)
						SCREAMING_SNAKE_CASE__						=					parquet_writer_kwargs
	def 	A_							(     self				:  Optional[int]				):
						SCREAMING_SNAKE_CASE__						=					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

						if isinstance(self.path_or_buf			,  (str, bytes, os.PathLike)				):
											with open(self.path_or_buf			,  'wb+'				) as buffer:
																SCREAMING_SNAKE_CASE__						=					self._write(file_obj=UpperCAmelCase_			,  batch_size=UpperCAmelCase_			,  **self.parquet_writer_kwargs				)
						else:
											SCREAMING_SNAKE_CASE__						=					self._write(file_obj=self.path_or_buf			,  batch_size=UpperCAmelCase_			,  **self.parquet_writer_kwargs				)
						return written





	def 	A_							(     self				:  List[Any]			,  UpperCAmelCase_				:  BinaryIO			,  UpperCAmelCase_				:  int			,  **UpperCAmelCase_				:  Union[str, Any]				):
						SCREAMING_SNAKE_CASE__						=					0
						SCREAMING_SNAKE_CASE__						=					parquet_writer_kwargs.pop('path_or_buf'			,  UpperCAmelCase_				)
						SCREAMING_SNAKE_CASE__						=					self.dataset.features.arrow_schema

						SCREAMING_SNAKE_CASE__						=					pq.ParquetWriter(UpperCAmelCase_			,  schema=UpperCAmelCase_			,  **UpperCAmelCase_				)

						for offset in logging.tqdm(
						    range(0			,  len(self.dataset				)			,  UpperCAmelCase_				)			,  unit='ba'			,  disable=not logging.is_progress_bar_enabled()			,  desc='Creating parquet from Arrow format'			,  ):
											SCREAMING_SNAKE_CASE__						=					query_table(
											    table=self.dataset._data			,  key=slice(UpperCAmelCase_			,  offset + batch_size				)			,  indices=self.dataset._indices if self.dataset._indices is not None else None			,  )
											writer.write_table(UpperCAmelCase_				)
											written += batch.nbytes
						writer.close()
						return written













"""simple docstring"""


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		import torch

		from transformers.modeling_outputs import BaseModelOutput

		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	Union[str, Any]     , **UpperCAmelCase							:	List[Any]   )	->       int:
											super().__init__(**UpperCAmelCase   )

											if self.framework == "tf":
																	raise ValueError(f"""The {self.__class__} is only available in PyTorch."""   )

											requires_backends(self     , 'vision'   )
											self.check_model_type(UpperCAmelCase   )
					def __call__(							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, "Image.Image", List[Dict[str, Any]]]     , UpperCAmelCase							:	Union[str, List[str]] = None     , **UpperCAmelCase							:	Dict     , )	->       Any:
											if "text_queries" in kwargs:
																	lowerCAmelCase   :Dict						=			kwargs.pop('text_queries'   )

											if isinstance(UpperCAmelCase     , (str, Image.Image)   ):
																	lowerCAmelCase   :List[str]						=			{'image': image, 'candidate_labels': candidate_labels}
											else:
																	lowerCAmelCase   :Union[str, Any]						=			image
											lowerCAmelCase   :Optional[int]						=			super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
											return results
					def        UpperCAmelCase__ (							self							:	List[Any]     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											lowerCAmelCase   :Any						=			{}
											if "threshold" in kwargs:
																	lowerCAmelCase   :int						=			kwargs['threshold']
											if "top_k" in kwargs:
																	lowerCAmelCase   :str						=			kwargs['top_k']
											return {}, {}, postprocess_params
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	Union[str, Any]   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(inputs['image']   )
											lowerCAmelCase   :Dict						=			inputs['candidate_labels']
											if isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																	lowerCAmelCase   :str						=			candidate_labels.split(','   )

											lowerCAmelCase   :str						=			torch.tensor([[image.height, image.width]]     , dtype=torch.intaa   )
											for i, candidate_label in enumerate(UpperCAmelCase   ):
																	lowerCAmelCase   :Any						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																	lowerCAmelCase   :Optional[Any]						=			self.image_processor(UpperCAmelCase     , return_tensors=self.framework   )
																	yield {
																	    "is_last": i == len(UpperCAmelCase   ) - 1,
																	    "target_size": target_size,
																	    "candidate_label": candidate_label,
																	    **text_inputs,
																	    **image_features,
																	}
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       List[Any]:
											lowerCAmelCase   :Union[str, Any]						=			model_inputs.pop('target_size'   )
											lowerCAmelCase   :List[str]						=			model_inputs.pop('candidate_label'   )
											lowerCAmelCase   :Union[str, Any]						=			model_inputs.pop('is_last'   )

											lowerCAmelCase   :int						=			self.model(**UpperCAmelCase   )

											lowerCAmelCase   :Any						=			{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	Optional[int]     , UpperCAmelCase							:	int=0.1     , UpperCAmelCase							:	Optional[Any]=None   )	->       str:
											lowerCAmelCase   :int						=			[]
											for model_output in model_outputs:
																	lowerCAmelCase   :int						=			model_output['candidate_label']
																	lowerCAmelCase   :List[Any]						=			BaseModelOutput(UpperCAmelCase   )
																	lowerCAmelCase   :Dict						=			self.image_processor.post_process_object_detection(
																	    outputs=UpperCAmelCase     , threshold=UpperCAmelCase     , target_sizes=model_output['target_size']   )[0]

																	for index in outputs["scores"].nonzero():
																							lowerCAmelCase   :Tuple						=			outputs['scores'][index].item()
																							lowerCAmelCase   :int						=			self._get_bounding_box(outputs['boxes'][index][0]   )

																							lowerCAmelCase   :Optional[int]						=			{'score': score, 'label': label, 'box': box}
																							results.append(UpperCAmelCase   )

											lowerCAmelCase   :Tuple						=			sorted(UpperCAmelCase     , key=lambda UpperCAmelCase   : x["score"]     , reverse=UpperCAmelCase   )
											if top_k:
																	lowerCAmelCase   :Dict						=			results[:top_k]

											return results
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	"torch.Tensor"   )	->       Dict[str, int]:
											if self.framework != "pt":
																	raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'   )
											lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			box.int().tolist()
											lowerCAmelCase   :Union[str, Any]						=			{
											    'xmin': xmin,
											    'ymin': ymin,
											    'xmax': xmax,
											    'ymax': ymax,
											}
											return bbox